Critical power adaptive training with varying parameters

ABSTRACT

An adaptive training system can determine a finite work capacity and critical power and use those determinations to generate an adaptive training programs for workout parameters. To determining a finite work capacity and critical power, the adaptive training system can receive workout data; generate data points by determining, for each workout, a maximum amount of work done for each of multiple time window sizes; fit these workout data points to a function; and determine a critical power and finite work capacity based on the fitted function. Once the adaptive training system has determined a critical power and finite work capacity, the adaptive training system can use them to generate an adaptive training program by keeping a function defined by a set of workout parameters below an ability function that is based on the critical power and finite work capacity.

BACKGROUND

Millions of people desire to train and get in better shape. For many,this is difficult due to lack of time or the effort involved. Manysystems have been developed to help people begin training or maximizetheir training efforts. For example, people read fitness magazines,follow workout programs, attend classes, hire personal trainers, etc.While some of these programs are tailored to individuals, they oftenfail to maximize a person's potential or cause frustration due tooverwork. One cause of this is that many of these programs are not basedon measures of a person's potential to perform exercise. Even forprograms that uses some previous method to tailor programs to anindividual, these methods has been difficult to implement and resultshave been inaccurate. Furthermore, training programs developed based onthese measurements have failed provide correct levels of intensity andduration.

For example, VO2 is a measurement system that determines a maximumamount of oxygen a person can use. VO2 is measured by dividing an amountof oxygen inhaled per minute by an amount of oxygen exhaled per minute.Thus, VO2 based assessments require a user to be attached to anapparatus that can measure air intake and output and an amount of oxygenin the air. Furthermore, VO2 measurements are generally static for anindividual and there is no clear way to translate a VO2 measurement intoa duration or intensity level for training. Some techniques have beendeveloped to provide workout guidelines based on heart ratemeasurements. However, these still require a specialized heart ratemonitor attached to a person, are inaccurate, and do not correctlyestimated limits on time or intensity training metrics.

SUMMARY

The present technology provides an adaptive training system and relatedmethods that overcome drawbacks of the prior art and provide otherbenefits. In at least one embodiment, an adaptive training system cangenerate training programs adapted to a particular user based oncritical power and varying workout parameters. A person's power outputis equal to their indefinite ability produce power (“critical power”) incombination with some finite capacity to work above their critical power(“finite work capacity”) over a given amount of time. A practicalapplication of a user's critical power and finite work capacity is usingthem to generate a user specific training program. A training programcan be a single training session or series of training sessions withspecified parameters based on a critical power or finite work capacitydetermination for a user. Training programs based on critical power areaccurate, require no special heart rate or oxygen measurement devices,and are adaptable to a user's changing ability level. Furthermore, thetransformations of critical power to a training program can be providedfor various types of exercises and timeframes.

The adaptive training system of one or more embodiments includes twoaspects of applying the critical power analysis: (1) determining aspecific user's finite work capacity and critical power withoutrequiring heart rate information or use of an invasive measurementapparatus, and (2) using those determinations to generate adaptivetraining programs, specific for the user, for any given set of workoutparameters. In determining a user's finite work capacity and criticalpower, the adaptive training system can receive statistics from one ormore of the user's current or prior workouts or segments of a workout,and determine the user's finite work capacity and critical power basedonly on time intervals and measurements indicative of power output (e.g.running speed, watts of stationary bike output, repetitions performedover a time, etc.). Furthermore, as a user logs additional workouts,these finite work capacity and critical power determinations can beupdated, and new or modified training programs can be generated for theuser. In some implementations, more recent workouts can have a greaterweight in determining these measurements as compared to older workoutstatistics. For example, a scalar decay function can be applied toworkout data to weight it based on age.

The adaptive training system can determine a finite work capacity andcritical power for a particular user by generating data points from oneor more workout statistic sets. Each data point can be a measure of theuser's maximum work performed in a workout for any time window of aparticular length. For a particular workout, multiple data points can betaken using different time window lengths. Each data point can be a workvalue computed by taking the maximum integration, for a given timewindow size, of a function that provides power based on time. Thus, awork data point will be the largest area under the power function forany interval matching the time window size of that data point. Wheremultiple workouts are used, a final workout statistic set can keep onlythe largest work value for each time window size from the multipleworkouts. The adaptive training system can fit points from one or moreworkout statistic sets to an ability function. The ability function canbe used to determine a critical power, which corresponds to the slope ofthe ability function, and the finite work capacity, which corresponds tothe y-intercept of the ability function. Alternatively, the adaptivetraining system can use the demonstrated work points in a final workoutstatistic set as boundary points for future training programs.Additional details regarding determining a user's ability functionand/or finite work capacity and critical power are discussed below inrelation to FIGS. 3-5.

Once the adaptive training system has determined a critical power andfinite work capacity, the adaptive training system can use thosedeterminations to generate an adaptive training program for a given setof workout parameters. For any particular workout type, a number ofparameters can describe the workout in terms of work and time. Forexample, in an interval workout with regular intervals, the workout canbe defined in terms of five parameters: number of intervals, intervalduration, power output during intervals, rest duration, power outputduring rests. All but one of these parameters can be specified either bya user or based on characteristics of the workout. For example, for aworkout on a treadmill, a user can specify interval duration and restduration, and a power output during the intervals and power outputduring rests can be determined based on the treadmill settings, such asspeed and angle. The adaptive training system can then compute the finalparameter, based on the user's critical power and finite work capacity,such that a user-specific workout plan can be generated that directs theuser's exercise to approach but not pass an exhaustion threshold. Forexample, the number of intervals can be set such that a function,describing the workout, based on the five parameters mapped in terms ofwork/time, does not exceed a user's ability function. In variousimplementations, the ability function can be provided by the user's workcapacity. In some implementations, the ability function can be a linearor non-linear function fit to a workout statistic set, as discussedabove. The slope of the ability function can define the user's criticalpower and the y-intercept can define the user's finite work capacity. Insome implementations, once a critical power (CP) and finite workcapacity (W′) have been determined, the ability function can be modifiedto account for real-world circumstances, such as the fact that givenzero time a user's ability to perform work is also zero. Thismodification can include turning the ability function into a combinedlinear and logarithmic function. For example, the ability function canbe specified as W=CP*t+W′*log(t+1), where W is a work ability (dependentvariable) and t is a given amount of time (independent variable). Insome implementations, the ability function can be the workout statisticset used as a boundary for corresponding points of training programs.Additional details regarding using an ability function or critical powerand finite work capacity to generate a specific training program for aworkout type defined by parameters are discussed below in relation toFIGS. 6 and 7.

In various implementations, the adaptive training system can beimplemented, at least in part, as an integrated component of a workoutmachine, in a network of workout machines that share information, as an“app,” in a fitness tracker, as part of a server-based informationsystem (e.g. accessed through a webpage), or any combination thereof.For example, the adaptive training system can be integrated into aworkout machine instrumented to take work measurements indicative ofpower output (e.g. resistance and revolutions per minute, time anddistance, etc.). The adaptive training system can use these measurementsto determine work vs. time data points. These data points can be used tocompute critical power and finite work capacity of the user (e.g. byfitting them to an ability function). The adaptive training system canthen provide a training program configured to bring the user within athreshold amount of, but not surpass, an exhaustion point determinedbased on the critical power and finite work capacity or abilityfunction. The training program can be provided as visual output withsuggested workout parameters. Alternatively or in addition, the trainingprogram can be provided as automatic adjustments to workout settings(e.g. treadmill speed). An example of this configuration is describedbelow in relation to FIG. 8A.

As another example, the adaptive training system can be implemented in anetwork of workout machines. In this example, the adaptive trainingsystem can identify the user based on authentication data provided to aworkout machine or through a mobile device, such as through a website orapp, by scanning a QR code on the machine, etc. Then, during asubsequent workout, data points from measurements on that machine can beadded to data points from measurements previously taken on othermachines to maintain updated critical power and finite work capacity orability function gauges. These can be used to generate a trainingprogram. The training program can include workout suggestions, which canbe provided on the networked machine or on the mobile device.Alternatively or in addition, the training program can include workoutmachine settings, which the adaptive training system can automaticallyapply on the machine. An example of this configuration is describedbelow in relation to FIG. 8B.

As yet a further example, the adaptive training system can beimplemented on a mobile device or other computing system, e.g. as anapp, through a webpage, or as a function of a smartwatch or otherfitness device. In some implementations, the adaptive training systemcan automatically gather data (e.g. distance, amount of time, speed,elevation change, etc.) or a user can manually enter it (e.g. byentering distance and time following a run). The adaptive trainingsystem can then provide a training program. In some implementations, thetraining program can include showing further statistics, such as changesin critical power or finite work capacity over time. In someimplementations, the training program can provide a suggested workoutplan. The suggested workout plan can set aspects of the training programto meet user goals. In some implementations, the training program caninclude guided workouts or other training suggestions or instructionsbased on the ability function or critical power and finite workcapacity. An example of this configuration is described below inrelation to FIG. 8C.

The current technology provides a number of benefits over the prior art.For example, the adaptive training system can determine a critical powerand finite work capacity without the need for an heart rate monitor,oxygen equipment, or other invasive equipment attached to a user. Inaddition, training programs based on critical power or finite workcapacity provide a basis for workout levels while not being subject tothe inaccuracies from which heart rate monitoring methods suffer.Further, the adaptive training system can be implemented using lowpower, low processing speed equipment and only needs to store the “bestrecord” data, effectively reducing the amount of required storagecapacity, as compared to prior art systems that require large amounts ofinterrelated data to make a training suggestion.

In various implementations, the adaptive training technology can beimplemented as a method, system, or computer-readable storage medium. Aworkout machine system for automatically providing an adaptive trainingprogram can include a workout apparatus that implements a trainingsession of a particular type, with given parameters. The workout machinesystem can also include an instrument system, integrated with theworkout apparatus, that obtains measurements indicative of power output.The workout machine system can also comprise a processing componentconfigured to generate an adaptive training program.

Where the adaptive training technology is implemented as a method it caninclude various operations and where the adaptive training technology isimplemented as a system or computer-readable storage medium a processingcomponent of the system or operating on the computer-readable storagemedium can perform the various operations. These operations cancomprise: identifying a work value for each particular window size, ofmultiple window size durations of the measurements indicative of poweroutput. This can be accomplished by identifying a largest integration,of a function specified by the measurements indicative of power output,for an interval matching the particular window size. The operations canalso include fitting, as an ability function, a function to the workvalues identified for the multiple window size durations. The operationscan further comprise computing a value for at least one previouslyunspecified parameter for a training session, such that values of aworkout function, that uses the previously unspecified parameter, do notexceed corresponding values of the ability function for any time duringa duration for the training session. The operations can also compriseusing the at least one previously unspecified parameter to generate theadaptive training program. The adaptive training technology can provideoutput or automatic workout settings based on the adaptive trainingprogram.

In some implementations, the adaptive training technology can include orimplement operations comprising obtaining given parameters for atraining session and obtaining measurements indicative of power output.The operations can further comprise generating the adaptive trainingprogram by: determining, based on the measurements indicative of poweroutput, an ability function or critical power assessment. Generating theadaptive training can further include using (A) the ability function orcritical power assessment and (B) the given parameters, to generate thetraining program. The training program can include a value for at leastone previously unspecified parameter. The operations can includeproviding this adaptive training program.

In various implementations, the adaptive training technology can obtainthe measurements indicative of power output through manual entry by auser, through recorded workout statistics taken by a wearable fitnesstracker, or through an instrument system integrated into a workoutapparatus.

In various implementations, the adaptive training technology can beimplemented by a server system connected to multiple workout machines,by one or more workout machines, or by a mobile device associated with auser of a workout apparatus.

In various implementations, the adaptive training technology can beimplemented in relation to multiple workout machines connected via anetwork. The measurements indicative of power output can be taken from afirst of the networked workout machines while a second set ofmeasurements indicative of power output can be taken from a second ofthe networked workout machines. This second set of measurementsindicative of power output can be transformed into additional workvalues for corresponding window sizes and can be weighted based on anage of the second set of measurements. When the adaptive trainingtechnology performs the fitting of the function to the work valuesidentified for the multiple window size durations, it can furtheraccomplish this by fitting the function to the additional work values.In some implementations, this fitting can include selecting, for eachparticular window size, the largest value, of the work values and to theadditional work values, corresponding to that particular window size;and fitting the function to the selected largest values for the windowsizes.

In some implementations, the adaptive training technology can generatethe adaptive training program in response to an identificationcomprising a code indicative of a user or of a workout apparatus. Theidentification can be supplied: by the user, by a mobile deviceassociated with the user, or by the workout apparatus. In someimplementations, the code indicative of the workout apparatus can besupplied to the mobile device through an image capture system on themobile device that captures an alphanumeric code, a bar code, or QR codedisplayed in conjunction with the workout apparatus.

The output or automatic workout settings that the adaptive trainingtechnology provides can be output to a server system comprising anindication of an additional determined parameter. Alternatively, theoutput can be provided to a mobile device comprising an indication of atleast one previously unspecified parameter. In some implementations, theoutput can be to a mobile device comprising data to be operated on bythe execution of instructions on the mobile device. This execution canprovide a display for a user to implement the adaptive training program.In some implementations, the output can be to a workout apparatusconfigured to cause a display of the workout apparatus to provideinstructions based on the adaptive training program. The output can alsoinclude automatic workout settings that cause a workout apparatus toautomatically implement the adaptive training program. In someimplementations, the output can be provided to a server system or amobile device and the output is stored in association with a userprofile. The user profile can include various information such as: theidentified work values, the ability function, statistics of the adaptivetraining program, measures of actual performance during the trainingsession, biographic specifics, or any combination thereof.

In some implementations, the adaptive training technology can provide aninternet-based interface that is accessible through a web browser ormobile device application. The internet-based interface can provideaccess to a user profile associated with multiple workouts performed bya user.

In various implementations, the measurements indicative of power outputcan comprise one or more of: speed, revolutions-per-minute, resistance,incline, distance, duration, weight, repetitions, or any combinationthereof.

In some implementations, the ability function is a linear function.

In some implementations, the adaptive training technology can beimplemented by a server system that obtains additional work values,stored in association with a user profile for the user, and generatesthe adaptive training program, both of which may be in response to anauthentication of the user.

In some implementations, the workout function can be defined for aninterval or skip interval type workout. Parameters for the workoutfunction for an interval workout can include the number of intervals,interval duration, power output during intervals, rest duration, poweroutput during rests, or any combination thereof. Parameters for theworkout function for a skip interval workout can include the number ofintervals, duration of peak work during interval, duration of short resttime within an interval, power output during interval peak, restduration between intervals, power output during rests, or anycombination thereof.

In some implementations, computing the value for the at least onepreviously unspecified parameter can comprise: obtaining the workoutfunction, from a set of pre-defined functions, based on an indicatedworkout type for the adaptive training program. The operations can thenfill in one or more given parameters in the workout function and solvingfor the at least one previously unspecified parameter. In someimplementations, generating the adaptive training program can compriseselecting data to display as instructions to a user to perform a workoutbased on at least one previously unspecified parameter. The adaptivetraining program can also specify the output or automatic workoutsettings based on the at least one previously unspecified parameter. Insome implementations, filling in parameters in the pre-defined functioncan comprise using one or more default values.

In some implementations, while the user is performing the adaptivetraining program, the adaptive training technology can also receivefurther measurements indicative of power output as the adaptive trainingprogram is performed. The adaptive training technology can identifyfurther work values based on the further measurements indicative ofpower output. The adaptive training technology can then update theability function to further fit the further work values; update one ormore parameters of the workout function based on a comparison of theworkout function to the updated ability function; generate an updatedadaptive training program based on the updated parameters; and updatethe output or automatic workout settings based on the updated adaptivetraining program.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating an overview of devices on whichsome implementations can operate.

FIG. 2 is a block diagram illustrating an overview of an environment inwhich some implementations can operate.

FIG. 3 is a block diagram illustrating components which, in someimplementations, can be used in a system employing the disclosedtechnology.

FIG. 4 is a flow diagram illustrating a process used in someimplementations for determining a finite work capacity and criticalpower using time-based power output statistics.

FIGS. 5A-E are conceptual diagrams illustrating an example of windowingpower output statistics to determine maximum work data points.

FIG. 5F is a conceptual diagram illustrating an example with a functionfit to a series of data points determined through windowing oftime-based power output statistics.

FIG. 5G is a conceptual diagram illustrating an example with a combinedlinear and logarithmic performance ability function generated based on adetermined finite work capacity and critical power.

FIG. 6 is a flow diagram illustrating a process used in someimplementations for translating a determined fit function into atraining program based on workout parameters.

FIG. 7 is a conceptual diagram illustrating an example representation ofa workout function computed by determining a parameter to keep theworkout function below an exhaustion point, based on fit functionparameters.

FIGS. 8A-C illustrate example system configurations that implementversions of an adaptive training system.

The techniques introduced here may be better understood by referring tothe following Detailed Description in conjunction with the accompanyingdrawings, in which like reference numerals indicate identical orfunctionally similar elements.

DETAILED DESCRIPTION

Turning now to the figures, FIG. 1 is a block diagram illustrating anoverview of devices on which some implementations of the disclosedtechnology can operate. The devices can comprise hardware components ofa device 100 that can assess critical power and use the assessment togenerate an adaptive training program. Device 100 can include one ormore input devices 120 that provide input to the CPU(s) (processor) 110notifying it of events. The events can be mediated by a hardwarecontroller that interprets the signals received from the input deviceand communicates the information to the CPU 110 using a communicationprotocol. Input devices 120 include, for example, instrumentation onworkout equipment that measures values indicative of power output (e.g.number of rotations, speed, incline, resistance, etc.), anaccelerometer, a pedometer, a touchscreen, an infrared sensor, awearable input device, a camera- or image-based input device, amicrophone, a mouse, a keyboard, or other user input devices.

CPU 110 can be a single processing unit or multiple processing units ina device or distributed across multiple devices. CPU 110 can be coupledto other hardware devices, for example, with the use of a bus, such as aPCI bus or SCSI bus. The CPU 110 can communicate with a hardwarecontroller for devices, such as for controlling settings of a workoutmachine or providing output to a display 130. Display 130 can be used todisplay text or graphics. In various implementations, display 130provides a training program as features of a suggested workout on aworkout machine display, on a phone display, on a fitness devicedisplay, or through a computer monitor display. In some implementations,display 130 includes the input device as part of the display, such aswhen the input device is a touchscreen or is equipped with an eyedirection monitoring system. In some implementations, the display isseparate from the input device. Examples of display devices are: an LCDdisplay screen, an LED display screen, a projected, holographic, oraugmented reality display (such as a heads-up display device or ahead-mounted device), and so on. Other input/output (“I/O”) devices 140can also be coupled to the processor, such as a network card, videocard, audio card, USB, firewire or other external device, camera,printer, speakers, CD-ROM drive, DVD drive, disk drive, or Blu-Raydevice. In some implementations, other I/O can include a haptic feedbacksystem, such as vibrations through a mobile device or fitness devicethat can provide indications of a training program. In someimplementations, other I/O can include a connection, either directly oracross a network, that provides for automatic controls and settings tobe provided to workout machines based on a determined training program.

In some implementations, the device 100 also includes a communicationdevice capable of communicating wirelessly or wire-based with a networknode. The communication device can communicate with another device or aserver through a network using, for example, TCP/IP protocols. Device100 can utilize the communication device to distribute operations acrossmultiple network devices.

The CPU 110 can have access to a memory 150 in a device or distributedacross multiple devices. A memory includes one or more of varioushardware devices for volatile and non-volatile storage, and can includeboth read-only and writable memory. For example, a memory can compriserandom access memory (RAM), CPU registers, read-only memory (ROM), andwritable non-volatile memory, such as flash memory, hard drives, floppydisks, CDs, DVDs, magnetic storage devices, tape drives, device buffers,and so forth. A memory is not a propagating signal divorced fromunderlying hardware; a memory is thus non-transitory. Memory 150 caninclude program memory 160 that stores programs and software, such as anoperating system 162, adaptive training system 164, and otherapplication programs 166. Memory 150 can also include data memory 170that can include power measurements, windowing data points, criticalpower or finite work capacity determinations, parameterizedtransformations for various workouts in terms of work, adaptive workoutprograms based on ability functions, configuration data, settings, useroptions or preferences, etc., which can be provided to the programmemory 160 or any element of the device 100.

Some implementations can be operational with numerous other computingsystem environments or configurations. Examples of computing systems,environments, or configurations that may be suitable for use with thetechnology include, but are not limited to, workout machines, personalcomputers, server computers, handheld or laptop devices, cellulartelephones, wearable electronics, gaming consoles, tablet devices,multiprocessor systems, microprocessor-based systems, set-top boxes,programmable consumer electronics, network PCs, minicomputers, mainframecomputers, distributed computing environments that include any of theabove systems or devices, or the like.

FIG. 2 is a block diagram illustrating an overview of an environment 200in which some implementations of the disclosed technology can operate.Environment 200 can include one or more computing enabled devices205A-D, examples of which can include device 100. For example, computingenabled devices can include any of a mobile phone or wearable device205A, laptop 205B, desktop or server 205C, or exercise equipment 205D,which can be stand alone or networked and configured to collect workoutdata. Computing enabled devices 205 can operate in a networkedenvironment using logical connections 210 through network 230 to one ormore remote computers, such as a server computing device.

In some implementations, server 210 can be an edge server which receivesclient requests and coordinates fulfillment of those requests throughother servers, such as servers 220A-C. Server computing devices 210 and220 can comprise computing systems, such as device 100. Though eachserver computing device 210 and 220 is displayed logically as a singleserver, server computing devices can each be a distributed computingenvironment encompassing multiple computing devices located at the sameor at geographically disparate physical locations. In someimplementations, each server 220 corresponds to a group of servers.

Computing enabled devices 205 and server computing devices 210 and 220can each act as a server or client to other server/client devices.Server 210 can connect to a database 215. Servers 220A-C can eachconnect to a corresponding database 225A-C. As discussed above, eachserver 220 can correspond to a group of servers, and each of theseservers can share a database or can have their own database. Databases215 and 225 can warehouse (e.g. store) information such as previousworkout data, user profile data, workout statistics, historical criticalpower and finite work capacity determinations, etc. Though databases 215and 225 are displayed logically as single units, databases 215 and 225can each be a distributed computing environment encompassing multiplecomputing devices, can be located within their corresponding server, orcan be located at the same or at geographically disparate physicallocations.

Network 230 can be a local area network (LAN) or a wide area network(WAN), but can also be other wired or wireless networks. Network 230 maybe the Internet or some other public or private network. Computingenabled devices 205 can be connected to network 230 through a networkinterface, such as by wired or wireless communication. While theconnections between server 210 and servers 220 are shown as separateconnections, these connections can be any kind of local, wide area,wired, or wireless network, including network 230 or a separate publicor private network.

FIG. 3 is a block diagram illustrating components 300 which, in someimplementations, can be used in a system employing the disclosedtechnology. The components 300 include hardware 302, general software320, and specialized components 340. As discussed above, a systemimplementing the disclosed technology can use various hardware includingprocessing units 304 (e.g. CPUs, GPUs, APUs, etc.), working memory 306,storage memory 308 (local storage or as an interface to remote storage,such as storage 215 or 225), and input and output devices 310. Invarious implementations, storage memory 308 can be one or more of: localdevices, interfaces to remote storage devices, or combinations thereof.For example, storage memory 308 can be a set of one or more hard drives(e.g. a redundant array of independent disks (RAID)) accessible througha system bus or can be a cloud storage provider or other network storageaccessible via one or more communications networks (e.g. a networkaccessible storage (NAS) device, such as storage 215 or storage providedthrough another server 220). Components 300 can be implemented in acomputing enabled device such as computing enabled devices 205 or on aserver computing device, such as server computing device 210 or 220.

General software 320 can include various applications including anoperating system 322, local programs 324, and a basic input outputsystem (BIOS) 326. Specialized components 340 can be subcomponents of ageneral software application 320, such as local programs 324.Specialized components 340 can include critical power (CP) and finitework capacity (FWC) analyzer 344, training program generator 346, andcomponents which can be used for providing user interfaces, transferringdata, and controlling the specialized components, such as interface 342.In some implementations, components 300 can be in a computing systemthat is distributed across multiple computing devices or can be aninterface to a server-based application executing one or more ofspecialized components 340.

In some implementations, critical power and finite work capacityanalyzer 344 can receive power output statistics for a user, e.g.through interface 342, and can use them to determine the user's criticalpower and finite work capacity. For example, where components 300 arepart of a workout machine, I/O 310 can include an instrument system,integrated with the workout machine, that takes measurements indicativeof power output. I/O 310 can then use interface 342 to provide themeasurements indicative of power output as power output statistics tocritical power and finite work capacity analyzer 344. As anotherexample, where components 300 are part of a mobile device, I/O 310 caninclude a user interface that takes user entered measurements indicativeof power output (e.g. run time and distance). I/O 310 can then useinterface 342 to provide these as power output statistics to criticalpower and finite work capacity analyzer 344. In some implementations,critical power and finite work capacity analyzer 344 can determine acritical power and finite work capacity by determining maximum work datapoints. Each maximum work data point can correspond to a largest areaunder a graph of the power output statistics for any window of a givensize. Each data point can represent an amount of work the user is ableto perform in a given timeframe. The critical power and finite workcapacity analyzer 344 can then fit a function, in the work range for thetime domain, to the highest data points for each window size acrossworkouts. In some implementations, critical power and finite workcapacity analyzer 344 can provide this fitted function in place of anexplicit critical power or finite work capacity values. In someimplementations where the fitted function is linear, the critical powercan be the slope of the fitted function and the finite work capacity canbe the y-intercept. In some implementations, once a critical power (CP)and finite work capacity (W′) have been determined, the ability functioncan be converted into a combined linear and logarithmic function. Forexample, the ability function can be specified as W=CP*t+W′*log(t+1),where W is a work ability (dependent variable) and t is a given amountof time (independent variable). Additional details on determiningcritical power and finite work capacity from power output statistics arediscussed below in relation to FIGS. 4 and 5.

In some implementations, training program generator 346 can generate auser-specific training program with at least one workout when a partialset of workout parameters and a user's critical power and finite workcapacity are given. In some implementations, the workout parameters canbe received through interface 342, e.g. based on user input, defaultvalues, specified goals, etc. The user's critical power and finite workcapacity can be determined by critical power and finite work capacityanalyzer 344. Training program generator 346 can determine the full setof workout parameters to customize the workout for the particular userby computing the missing parameter that keeps values of a functiondefining the workout below values of a function defining the user'sability (e.g. the function fit to the data points by critical power andfinite work capacity analyzer 344). Additional details on generate atraining program with at least one user specific workout based on acritical power and finite work capacity are discussed below in relationto FIGS. 6 and 7.

Those skilled in the art will appreciate that the components illustratedin FIGS. 1-3 described above, and in each of the flow diagrams discussedbelow, may be altered in a variety of ways. For example, the order ofthe logic may be rearranged, substeps may be performed in parallel,illustrated logic may be omitted, other logic may be included, etc. Insome implementations, one or more of the components described above canexecute one or more of the processes described below.

FIG. 4 is a flow diagram illustrating a process 400 used in someimplementations for determining an ability function or user's finitework capacity and critical power based on time-based power outputstatistics. Process 400 begins at block 402 and continues to block 404.In some implementations, process 400 can be performed continuously, e.g.as workout information is fed into an adaptive training system duringthe workout. In some implementations, process 400 can be performed aheadof time, e.g., based on a received log of the user's workout data.

At block 404, process 400 can obtain time-based power output statisticsfor a workout or set of workouts. In various implementations, the poweroutput statistics can be automatically gathered from instrumentation ona workout apparatus (e.g. treadmill, elliptical, bike, fitness tracker,etc.), can be entered through a user interface (e.g. entering a runduration and distance), or can be a log of one or more previouslyrecorded workouts. In some implementations, power data can be based oncombinations of measures such as speed or distance and time, where adefault force for a workout type can be used to estimate work per time,i.e. power. In some implementations, the force can also be, at least inpart, measured, e.g. based on a resistance or incline setting. In someimplementations, user biographic specifics such as height, weight,gender, and age, are provided in a user profile. These user biographicscan be used to more accurately convert workout measurements, such asdistance run in an amount of time, into a power measurement. However,process 400 can use some default values to determine power measurementsbased on a user's workout performance, and thus generate a user'scritical power and finite work capacity without the user's biographics.This allows users to use these versions of the system without requiringan extensive profile creation process, and thus users have a low barrierto entry for the system.

In the outer loop between blocks 406-418, process 400 can iterativelydivide the power output statistics into segments of the time domain, or“windows,” where the window size increases by an incremental amount ineach iteration of this outer loop. For all of the windows with aparticular size, the window with a largest determined integrated power(work) value in a particular iteration can result in a data point. Thework value for a particular window can be computed by taking the areaunder the power curve defined by the power output statistics in thattime window. The largest of these work measures for any window of aparticular size is an estimation of the maximum amount of work that usercan perform in the amount of time defined by the window size. Theresulting set of data points can be used to determine an abilityfunction, which can in turn be used to determine a critical power andfinite work capacity for that user.

At block 406, process 400 can set a window size and a window increment.A window size can be an interval of time from which an individual datapoint can be extracted from the obtained power output statistics. Forexample, if the obtained power output statistics provide power outputvalues at increments of a 30 minute workout, a window size can be thesize of various “windows” of the 30 minute dataset. One window willproduce a stored data point from each set of windows that all have thesame size. The window increment can be a value by which the start andend of a previous window are incremented to obtain a next window. Insome implementations, windows can be overlapping, meaning the windowincrement is set to a value less that the window size. In someimplementations, the windows can be non-overlapping, meaning the windowsize and window increment are set to the same value. In each iterationof the loop between blocks 406-418, process 400 can generate a singledata point corresponding to the window size used in that iteration. Ineach subsequent iteration, a new window size is selected at block 406.In some implementations, these window sizes can start at a particularsize (e.g. 1 second, 5 seconds, etc.) and be increased by a set amountor percentage in each subsequent iteration of the loop. For example, thefirst window size could be 1 second and window sizes are increased by10% in each subsequent iteration, so the first five window sizes wouldbe 1 second, 1.1 seconds, 1.21 seconds, 1.33 seconds, and 1.46 seconds.As another example, the first window size could be 5 seconds and windowsizes are increased by 1 second in each subsequent iteration, so thefirst five window sizes would be 5 seconds, 6 seconds, 7 seconds, 8seconds, and 9 seconds.

At block 408, process 400 can set an initial window for the currentwindow size by starting the window at the start of the obtainedtime-based power output statistics (e.g. at time_zero) and ending thewindow at the start of the obtained time-based power output statisticsplus the window size (e.g. time_zero+window_size). In the inner loopbetween blocks 410-414, process 400 can slide the window for the currentwindow size over the time-based power output statistics to determine (atblock 416) at which point the area under the curve, defining the poweroutput statistics, within the window size, is largest. While process 400describes this in terms of incrementing a window's start/end points,other procedures can be used to determine which area under the curve,for the window size interval, is largest. At block 410, process 400 canrecord an integrated power (work) value from the current window, i.e.the integration or area under the curve for the power/time graph of theobtained power-based statistics. An example of this process is providedbelow in relation to FIGS. 5A-E. Each recorded value can be a pointhaving window size (e.g. seconds) as the x-axis and work done (e.g.joules) as the y-axis.

At block 412, process 400 can determine if the end of the current windowis at least at the end of the obtained time-based power outputstatistics. If so, each window of the current window size into thetime-based power output statistics has been analyzed, the inner loopbetween blocks 410-414 is complete, and process 400 continues to block416. If not, at least one window into the time-based power outputstatistics has not yet been analyzed, the inner loop between blocks410-414 is not complete, and process 400 continues to block 414. Atblock 414, process 400 can increment the start and end of the currentwindow by the window increment. This creates a new current window for avalue to be recorded from, at block 410.

At block 416, process 400 can review all the values recorded at block410 for the current window size and store the largest valuecorresponding to the current window size. This value indicates a measureof the most amount of work the user can perform in a given amount oftime. In some implementations, the value stored corresponding to thecurrent window size can be the largest of the recorded values, anaverage of a top amount (e.g. top 5 or top 10%) of the recorded values,or the top value that is within a certain standard deviations of themedian of the recorded values. In some implementations, once a value isstored at block 416, the memory storing the values recorded at block 410can be freed.

At block 418, process 400 can determine if there are additional windowsizes to use in a further iteration of the loop between blocks 406-418.For example, process 400 can use a specified number of window sizes, canincrement the window sizes a certain amount until a threshold is reachedor until the window size is at least a specified percentage of the totaltime covered by the time-based power output statistics, or can have aspecified set of window sizes to use. If there are additional windowsizes to use, process 400 continues back to block 406, where the nextwindow size is set. Otherwise, process 400 continues to block 420.

At block 420, process 400 can fit a function to the values stored atblock 416. This function can have the window size (e.g. seconds) on thex-axis and work (e.g. joules) on the y-axis. In various implementations,the function can be of a specified degree, e.g. a first degree function(linear), a second degree function (quadratic), third degree (cubic),etc. In some implementations, in addition to the data points stored atblock 416 for the obtained time-based power output statistics, thefunction can be fit to additional stored data points, e.g. from otherworkout statistics that were windowed in a process similar to the onedescribed for blocks 404-418. In some implementations, process 400 canfit a function to data points that are not from a windowing process. Forexample, where power output statistics are not known for multiple pointsacross a workout, work vs. time measures from single workout instancescan be used as data points and the function is fit to this set of datapoints.

In some implementations, various procedures can be applied to adjust forthe age of the stored power output values or anomalies among the storedpower output values. In some implementations, this adjustment caninclude smoothing the curve of the function, e.g. applying a Gaussiankernel. In some implementations, this adjustment can include limitingthe number of data points that are looked at for a given window size.For example, only data points for a window size that are no more thansix weeks old are used or only the most recent six data points are used.In some implementations, the value of each data point can be weighted(decayed) based on its age. Various decay functions can be used toaccomplish the weighting, such as exponential decay. For example, thevalue of each data point can be multiplied by 0.99̂(age_in_weeks). Asanother example, each data point can be multiplied by ê(V*age), where Vcan be a value less than 1, such as 0.0004, and the age can be invarious increments, such as days since the workout that generated thedata point.

In some implementations, where there are multiple data points for asingle window size (e.g. from different workouts), the function can befit to only the largest work data point for each window size (or thelargest after any applied weighting). In some implementations, once afunction is fit to the stored data points, the memory storing datapoints that were not used as part of the fit, e.g. because there was alarger data point for the same window size, can be freed.

The function fit to the data points representing a user's maximum poweroutput ability in a timeframe can specify a critical power (CP) for theuser. This fitted function can also specify a finite work capacity (W′)for the user. The critical power can be the slope of the function or,where the function is non-linear, a slope of the function between twopoints of the function. Thus, the critical power can represent an amountof additional work the user can perform given any specified amount ofadditional time. The finite work capacity can be the y-intercept (a workoutput, e.g. joules, value) of the function. Thus, the finite workcapacity represents an amount of initial work the user can perform givenno extra time.

In some implementations, the determined critical power and finite workcapacity can be used to generate a new ability function that moreaccurately defines the user's abilities, particularly in early intervalsof a workout, such as in the first 60 seconds. For example, a functioncan be more accurate where it accounts for real-world circumstances suchas the fact that given zero time a user's ability to perform work isalso zero. In some implementations, this new ability function can be acombined linear and logarithmic function. For example, the abilityfunction can be specified as W=CP*t+W′*log(t+1), where W is a workability (dependent variable) and t is a given amount of time(independent variable). Using this new ability function provides morestable and robust fit optimization as it accounts for the time dependentcurvature seen in the data.

Process 400 can return these determined critical power or finite workcapacity values and end at block 422. In some implementations, insteadof determining and returning critical power and finite work capacityvalues, the function fit to the data points can be returned.

FIGS. 5A-E are conceptual diagrams illustrating an example of windowingpower output statistics to determine maximum work data points. Thesemaximum work data points can then be used to compute an abilityfunction. FIGS. 5A-E show an example implementation of the loop betweenblocks 406-418. FIG. 5A shows step 500 of this example where poweroutput statistics for a workout have been graphed as function 502 andthe window size has been set to six seconds. A particular window 504 hasbeen determined to correspond to the largest area 506, under function502, for a six second window size. A data point corresponding to thislargest area 506 is added to the work vs. time graph 512, at theintersection of line 506 (six seconds—the window size) and line 510 (360joules—area 506).

FIG. 5B shows step 515 of this example. A next window 518 has beendetermined to correspond to the largest area 520, under function 502,for a ten second window size. A data point corresponding to this largestarea 520 is added to the work vs. time graph 512, at the intersection ofline 522 (ten seconds—the window size) and line 524 (400 joules—area520).

FIG. 5C shows step 530 of this example. A next window 534 has beendetermined to correspond to the largest area 536, under function 502,for a twelve second window size. A data point corresponding to thislargest area 536 is added to the work vs. time graph 512, at theintersection of line 538 (twelve seconds—the window size) and line 540(500 joules—area 536).

FIG. 5D shows step 545 of this example. A next window 548 has beendetermined to correspond to the largest area 550, under function 502,for a forty second window size. A data point corresponding to thislargest area 550 is added to the work vs. time graph 512, at theintersection of line 552 (forty seconds—the window size) and line 554(1667 joules—area 550).

FIG. 5E shows step 560 of this example. A next window 562 has beendetermined to correspond to the largest area 564, under function 502,for an eighty-four second window size. A data point corresponding tothis largest area 564 is added to the work vs. time graph 512, at theintersection of line 566 (eighty-four seconds—the window size) and line568 (2065 joules—area 564).

Once the data points identified in FIGS. 5A-E are known, they can beused to define an ability function. In some implementations, work vs.time graph 512 can be the user's ability function. In someimplementations, the ability function can be a border specified by thesedata points, where the border can limit maximum workout parameters asdescribed in relation to FIG. 6. In some implementations, anotherfunction can be fit to these data points to define the ability function.In some implementations, the fitted function can then be used todetermine a critical power and finite work capacity. FIGS. 5A-E providesample window sizes from a single workout. In other embodiments, windowsizes can be larger or smaller or can be more or less numerous. Anexample is provided next in FIG. 5F that uses other data points frommultiple workouts, with more window sizes, to generate an abilityfunction.

FIG. 5F is a conceptual diagram illustrating an example 580 with afunction fit to a set of data points for determining a user's criticalpower and finite work capacity. The function is computed by determininga fit to a series of data points that were obtained through windowing oftime-based power output statistics, as shown in FIG. 4. FIG. 5F showswork 582 (y-axis, in joules) vs. time 584 (x-axis; in seconds).

Example 580 includes a set of data points, such as data point 586. Eachdata point is the largest work value data point, from a set of possiblework value data points each corresponding to a particular window size,from a workout. In example 580, a set of possible data points (notshown) were taken at block 410 with a window size of 1150 seconds, andone representing the largest amount of work done (i.e. area under thepower output curve) in an 1150 second window of the workout was storedat block 416, as data point 586. An example of this process is providedin FIGS. 5A-E. The additional data points at the 1150 seconds mark aboveand below data point 586 are from statistics taken from other workouts.

In example 580, line 590 was a best-fit to the largest of the datapoints for each window size from the various workouts. In someimplementations, process 400, at block 420, can determine where aperformance boundary is for a user as a location, after an initialthreshold, where further data points no longer increase in value with aslope that is relatively consistent. This demonstrated performanceboundary is shown in example 580 by dashed line segments 592. The fit ofline 590 is fit to the largest of the data points for each window sizebefore the split at the beginning of the performance boundary. The slopeof line 590 is used as the critical power. Example 580 also shows where,at 594, line 590 crosses the y-axis 582, which is used as the finitework capacity.

FIG. 5G is a conceptual diagram illustrating an example with a combinedlinear and logarithmic performance ability function generated based on adetermined finite work capacity and critical power. In this example, acritical power (CP) and finite work capacity (W′) have been determined.These values are used to generate a new ability function 590, defined bythe formula W=CP*t+W′*log(t+1), where W is a work ability (dependentvariable) and t is a given amount of time (independent variable). Thisnew ability function 590 is a better fit to the performance boundary592, particularly in the first 60 seconds of the work data.

FIG. 6 is a flow diagram illustrating a process 600 used in someimplementations for determining a user-specific training program bydetermining workout parameters based on the user's determined criticalpower or ability function. In some implementations, process 600 can beperformed continuously, e.g. as workout information is fed into anadaptive training system during the workout. In some implementations,process 600 can be performed ahead of time, e.g. to generate a trainingsession for a user prior to the user beginning the workout. Process 600begins at block 602 and continues to block 604.

An adaptive training program can include one or more workouts and eachworkout can be defined by a set of parameters mapping the workout intowork values given time. For example, when defining a sprint exercise fora user, the parameters can be simply duration and power output duringthe sprint. As another example, for an interval workout with regularintervals, the parameters can be: number of intervals, intervalduration, power output during intervals, rest duration, and power outputduring rests. As a further example, for an interval workout with “skipintervals,” the parameters can be: number of intervals, duration of peakwork during interval, duration of short rest time within an interval,power output during interval peak, rest duration between intervals, andpower output during rests. In some implementations, workouts can alsoinclude a difficulty factor parameter, which specifies how close to theuser's maximum work capacity the workout should bring the user.

At block 604, process 600 can receive a partial set of parameters for aworkout. In some implementations, the parameters received at block 604can be all but one of the parameters needed to specify the workout. Forexample, for the interval workout with regular intervals, all but theparameter defining the number of intervals can be provided. In someimplementations, one or more of the workout parameters can be set byvarious methods, such as by user input, using default values, usingvalues established to further certain goals (e.g. increase criticalpower ability; increase finite work capacity ability; increase sprintability by having shorter more intense workouts; or endurance withlonger, less intense workouts), etc. In some implementations, theworkout parameters can be restricted to certain values, e.g. maximum orminimum time or number or duration of intervals, etc. These restrictionscan be in place so that the remaining parameter determined by the systemis realistic (i.e. it doesn't suggest a workout of two days or of twelveseconds) or so that a workout based on the parameters is easier tofollow (e.g. interval length is a round number).

At block 606, process 600 can use the received workout parameters and auser ability indicator comprising one or more of: critical power andfinite work capacity values for a user, a “fit function” defined by afunction fit to a set of workout statistics in work vs. time, or the setof workout statistics in work vs. time as a defined border. In someimplementations, the user's user ability indicators can be computed eachtime they are needed, e.g. by executing process 400. In someimplementations, instead of recomputing the user's ability indicatorsfrom all available workout data, stored previous versions of the user'sability indicators can be updated to account for new workout data thathas been obtained since that stored ability indicators were generated.For example, the stored user's ability indicators can be weighted basedon the age of the underlying data and can be updated with each workoutset. In various implementations, this updating can be performed togenerate new user ability indicators when new workout data is received,when a determination of a user's ability indicators are needed, at agiven interval (e.g., daily, weekly, etc.), or based on availableresources (e.g., at night when server availably is typically high,according to a current measure of server resource availably, etc.).

Process 600 can then use the workout parameters and user abilityindictor to compute the missing parameter defining the workout to bespecific for the user. For each type of workout, a transform can bedetermined to map a function defining the workout (based on the workoutparameters) in terms of work vs. time. The function defining the workoutcan be specified such that it does not exceed an “ability line” for theuser, defined by the received user ability indicator. In variousimplementations, the ability line can be the ability function, thedefined border, or a line determined by setting the user's criticalpower as the slope and the user's finite work capacity as they-intercept. The point where the workout function intercepts the abilityline is the exhaustion point, i.e. the point where the user is expectedto no longer be able to perform any work at the same intensity level.Examples of such parameterization and comparison are provided below inrelation to FIG. 7 and the Appendix. In some implementations, theability line can be decreased (the y-intercept can be decreased with thesame slope) so that the user is only expected to come within a thresholdamount of a total exhaustion point. In some implementations, a defaulty-intercept can be set, (e.g. 0). For example, this can be done whenonly the critical power of a user is available or where it will be aparticularly lengthy workout.

At block 608, process 600 can provide a user specific training programbased on the now complete set of workout parameters. For example,process can indicate a workout duration, can cause a setting to beestablished on a workout machine, can integrate the workout into a setof multiple workouts, etc. In some implementations, process 600 cancontinually monitor power output during the user's workout, update theuser's ability function for the observed power output, and update theworkout parameters accordingly. Process 600 can then provide furtherupdates to the user, such as suggesting they slow down if they areexpected to hit their exhaustion point before the workout is scheduledto end or that they should speed up if they are not expected to reach agoal.

FIG. 7 is a conceptual diagram illustrating an example 700representation of a workout function 702 for a user specific workout.The user specific workout is computed by determining a time parameter704 which keeps all values of the workout function 702, during theworkout, below the user's ability line 706.

In example 700, a user's critical power and finite work capacity areknown, e.g. through an execution of process 400. The user's ability line706 is generated by setting the critical power as the slope 720 and thefinite work capacity as the y-intercept 718.

In example 700, the workout is an interval workout with regularintervals. The duration of the rest period 710 and high intensity period712 are set by a user through a user interface and the slope of the linesegments 714 during the high intensity segments are set to a firstdefault value based on an expect running rate during these highintensity portions the slope of the line segments 716 during the restsegments are set to a second default value based on another expectrunning rate during the rest portions.

The adaptive training system determines the intersection between theuser's ability line and the function representing the workout, i.e.exhaustion point 708. The time value 704 for the exhaustion point is thetime determined for this workout such that the user reaches herexhaustion point. Additional details on how the adaptive training systemcan perform these operations are provided in the appendix.

FIGS. 8A-C illustrate examples 800, 820, and 860, showing systemconfigurations that implement versions of an adaptive training system.In FIG. 8A, example 800 shows a configuration where the adaptivetraining system is integrated into workout machine 810. Example 800begins with measurements indicative of a user's power output, such asspeed and incline, being recorded during a workout session. At 804,circuitry in the workout machine transforms the power measurements intovalues of work. The circuitry then uses these values of work todetermine a user's ability function and uses the ability function togenerate a training program. In some implementations, step 804 can beaccomplished using processes 400 and 600. For example, workout machine810 can use the power measurements to determine work points and then fitthe work points to an ability line. The workout machine can use theability line and parameters that the user has specified for the workoutto generate a training program specifying other, previously unknownworkout parameters that keeps the user below his or her expectedexhaustion point. For example, the user can specify parameters such asduration and interval length and the workout machine can generate atraining program specifying an incline for each interval that keeps theuser below his or her expected exhaustion point.

The training program can be used, at 806, to automatically controlfunctionality of the workout machine, such as by setting speed,duration, or incline settings. In addition or alternatively, theresulting training program can be used, at 806, to provide an output ona display of the workout machine or associated device (e.g. a user'spaired mobile device). The output can display statistics about theworkout, instructions for performing the training program, etc. Whileexample 800 can end when the workout session ends, in someimplementations, data from the workout session can be stored at 808. Forexample, this data can include determined maximum work points, anability function, workout duration, speed, calories burned, etc. Thisdata can be sent to the user's mobile device or to a server. The systemcan store this output in association with a user profile.

In FIG. 8B, example 820 shows a configuration where the adaptivetraining system is implemented in a network of workout machines, such asworkout machines 822 and 826. In example 820, the workout machines areconnected via server system 824. In example 820, a user previously usedworkout machine 822. Workout machine 822 can send, at 842, anidentification of the user and data from that workout to server system824. For example, this data can include determined maximum work points,an ability function, workout duration, speed, calories burned, etc. At844, server system can use the received workout data to determine a userability function. In some implementations, step 844 can also includedetermining a training program for the user.

At step 846, the same user authenticates himself or herself with anotherworkout machine 826. For example, the user can enter a code, scan abarcode on the workout machine using her mobile device (e.g. mobiledevice 828), or the workout machine can take a reading, such as scanninga code presented by the user, recognizing the presence of the user'smobile device or a FOB, etc. At 848, workout machine 826 and serversystem 824 can communicate to determine a training program for theauthenticated user. In some implementations, the training program can bethe one determined at 844, which can be further based on user profileinformation, such as preferred workout parameters that may includeduration, number of intervals, etc. In some implementations, thecommunication at 848 can include some parameters for the user's nextworkout, which the server system 824 can use to generate the trainingprogram such that the user approaches, but does not surpass, anexhaustion point.

As the user performs the training program, which can be implementedautomatically by the workout machine 826 or can be accomplished throughinstructions provided to the user on how to interact with the workoutmachine, (not shown), additional measurements indicative of power outputcan be taken at step 850. In some implementations, the workout machine826 can autonomously update the training program or workout parametersbased on the power measurements, similar to the functions performed byworkout machine 810 in example 800. In some implementations, the powermeasurements can be provided, at 852, to server system 824. The serversystem, at 854, can update the user's determined ability function. Alsoat 854, the server system can adjust the training program based on theupdated user's ability function. The server system, at 856, can providethe updated training program or workout parameters to the workoutmachine 826. At 858, workout machine 826 can implement the updatedworkout parameters, e.g. by providing an updated display on workoutmachine 826 or on the user's mobile device 828, or by automaticallyimplementing settings on workout machine 828. Also at 858, either as theworkout progresses or when the workout ends, workout machine 826 canprovide workout data, similarly to step 808 of example 800.

In FIG. 8C, example 860 shows a configuration where the adaptivetraining system is implemented on mobile device 864. Example 860 beginswhen mobile device 864 receives workout statistics. In example 860, theworkout statistics are obtained from communications with a fitnesstracker, however the workout statistics can be obtained from othersources, such as from communication with another type of workoutmachine, manual user input, logs stored on a server system, etc. At 872,mobile device 864 transforms the workout statistics (e.g. powermeasurements) into terms of work, uses these to determine a user'sability function, and uses the ability function to generate a trainingprogram for the user. In some implementations, step 874 can beaccomplished using processes 400 and 600. For example, mobile device 864can use the power measurements to determine work points and then fit thework points to an ability function. Using the ability function, mobiledevice can generate a training program, e.g. to determine a run durationor suggested speed, interval lengths, etc. In some implementations, step874 can be accomplished using processing from a server system, such asserver system 866.

The received workout statistics, transformations of these statistics, orresulting training programs can be stored by mobile device 864. Whileexample 860 can end here, e.g. where user data is all stored at themobile device and training program suggestions are provide through themobile device, in some implementations, this data can be sent, at 876,to other devices 866-870, e.g. for further analysis (e.g. at serversystem 866), for automatic control of workout equipment 870, or to beviewed by the user, e.g. through a web interface at a computing device868. In various implementations, these interactions can be interrelatedor facilitated through communications with other devices, e.g. throughcommunications 874.

In various implementations, aspects of the configurations from any ofexamples 800, 820, and 860 can be combined. For example, input 872 inexample 860 can be based on outputs 808 or 858 from examples 800 and820; server 824 in example 820 in example 820 could be replaced withmobile device 864 from example 860; output 842 in example 820 can bebased on output 808 in example 800 or can be output 876 from example860. Additional configurations, combinations, substitutions andadditions are also contemplated. While examples 800, 820, and 860illustrate several types of devices, in some implementations, thefunction performed by any one of these devices can be accomplished bythe others or by devices not explicitly recited.

The following is a non-exhaustive list of additional examples of thedisclosed technology.

1. A workout machine system for automatically providing an adaptivetraining program, the system comprising:

-   -   a workout apparatus that implements a training session of a        particular type, with given parameters;    -   an instrument system, integrated with the workout apparatus,        that obtains measurements indicative of power output; and    -   a processing component configured to generate the adaptive        training program by:        -   identifying a work value for each particular window size, of            multiple window size durations, within the measurements            indicative of power output, by:            -   identifying a largest integration, of a function                specified by the measurements indicative of power                output, for an interval matching the particular window                size;        -   fitting, as an ability function, a function to the            identified work values;        -   applying, to a workout function defined for the particular            type of the training session, the given parameters and at            least one additional parameter, such that the value of the            workout function does not exceed the value of the ability            function for any time during a duration for the training            session; and        -   using the given parameters and at least one additional            parameter to generate the adaptive training program;    -   wherein output or settings of the workout apparatus are        automatically provided based on the adaptive training program.

2. The workout machine system of example 1,

-   -   wherein the workout machine system further comprises multiple        workout machines connected via a network; and    -   wherein the fitting the function to the work values identified        for the multiple window size durations further comprises also        fitting the function to additional work values identified for        the multiple window size durations based on measurements        indicative of power output taken on one of the networked workout        machines other than the workout apparatus.

3. The workout machine system of example 2, wherein the fitting thefunction to the work values and to the additional work values comprises:

-   -   selecting, for each particular window size, the largest value,        of the work values and to the additional work values,        corresponding to that particular window size; and    -   fitting the function to the selected largest values for the        window sizes.

4. The workout machine system of any one of examples 1-3, wherein theprocessing component is a server system connected to the network.

5. The workout machine system of any one of examples 1-4, wherein theprocessing component generates the adaptive training program in responseto an identification comprising:

-   -   a code indicative of a user, supplied by the user or by a mobile        device associated with the user, to the workout apparatus or to        the processing component; or    -   a code indicative of the workout apparatus, supplied by the user        or by the mobile device associated with the user, to the        processing component.

6. The workout machine system of example 5,

-   -   wherein the processing component generates the adaptive training        program in response to the identification comprising the code        indicative of the workout apparatus; and    -   wherein the code indicative of the workout apparatus is supplied        to the mobile device through an image capture system on the        mobile device capturing an alphanumeric code, bar code, or QR        code displayed in conjunction with the workout apparatus.

7. The workout machine system of example 5,

-   -   wherein the processing component generates the adaptive training        program in response to the identification comprising the code        indicative of the user; and    -   wherein the code indicative of the user is supplied by the        mobile device, through a wireless communication, to the workout        apparatus.

8. The workout machine system of any one of examples 1-3 or 7, whereinthe processing component is incorporated in the workout apparatus.

9. The workout machine system of any one of examples 1-3 or 7, whereinthe processing component is incorporated in a mobile device associatedwith a user of the workout apparatus.

10. The workout machine system of any one of examples 1-9, wherein theautomatically providing the output or settings of the workout apparatuscomprises providing the output to a server system, the output comprisingat least an indication of the at least one additional parameter.

11. The workout machine system of any one of examples 1-10, wherein theautomatically providing the output or settings of the workout apparatuscomprises providing output to a mobile device, the output comprising atleast an indication of the at least one additional parameter.

12. The workout machine system of any one of examples 1-11, wherein theautomatically providing the output or settings of the workout apparatuscomprises providing the output to a mobile device, the output comprisingdata to be operated on by execution of instructions on the mobiledevice, configured to provide a display for a user to implement theadaptive training program.

13. The workout machine system of any one of examples 1-12, wherein theautomatically providing the output or settings of the workout apparatuscomprises providing the output to the workout apparatus, the outputconfigured to cause a display of the workout apparatus to provideinstructions for the adaptive training program.

14. The workout machine system of any one of examples 1-13, wherein theautomatically providing the output or settings of the workout apparatuscomprises providing the settings to the workout apparatus, wherein theworkout apparatus automatically implements the adaptive training programbased on the settings such that the workout apparatus implements thegiven parameters and the at least one additional parameter.

15. The workout machine system of any one of examples 1-14, wherein theautomatically providing the output or settings of the workout apparatuscomprises providing the output to a server system or a mobile device,wherein the output is stored in association with a user profilecomprising one or more of:

-   -   at least some of the identified work values;    -   the ability function;    -   statistics of the adaptive training program;    -   measures of actual performance during the training session;    -   biographic specifics; or    -   any combination thereof.

16. The workout machine system of any one of examples 1-15, furthercomprising an internet-based interface, accessible through a web browseror mobile device application, wherein the internet-based interfaceprovides access to a user profile associated with multiple workoutsperformed by a user.

17. The workout machine system of any of any one of examples 1-16,wherein the measurements indicative of power output comprise one or moreof:

-   -   speed;    -   revolutions-per-minute;    -   resistance;    -   incline;    -   distance;    -   duration; or    -   any combination thereof.

18. The workout machine system of any one of examples 1-17 wherein theability function is a linear function.

19. The workout machine system of any one of examples 2 or 3,

-   -   wherein the processing component is a server system connected to        the network; and    -   wherein the server system obtains the additional work values        stored in association with a user profile for the user and        generates the adaptive training program, both in response to an        authentication of the user.

20. The workout machine system of any one of examples 1-19, wherein theparticular type is an interval type workout.

21. The workout machine system of example 20, wherein the givenparameters for the interval type workout include one or more of: numberof intervals, interval duration, power output during intervals, restduration, power output during rests, or any combination thereof.

22. The workout machine system of any one of examples 1-19, wherein theparticular type is a skip interval type workout.

23. The workout machine system of example 22, wherein the givenparameters for the skip interval type workout include one or more of:number of intervals, duration of peak work during interval, duration ofshort rest time within an interval, power output during interval peak,rest duration between intervals, power output during rests, or anycombination thereof.

24. The workout machine system of any one of examples 1-23,

-   -   wherein applying, to the workout function defined for the        particular type of the training session, the given parameters        and at least one additional parameter comprises:        -   obtaining a pre-defined function for the particular type of            the training session;        -   filling in the given parameters in the pre-defined function;            and        -   solving for the at least one additional parameter, wherein            the at least one additional parameter includes exactly one            additional parameter; and    -   wherein using the given parameters and at least one additional        parameter to generate the adaptive training program comprises:        -   selecting data to display instructing a user to perform a            workout based on at least the given parameters and the            solved at least one additional parameter; or        -   specifying the settings of the workout apparatus based on at            least the given parameters and the solved at least one            additional parameter.

25. The workout machine system of example 24, wherein filling in thegiven parameters in the pre-defined function comprises using one or moredefault values for one or more of the function parameters that are notincluded in the given parameters and are not included in the at leastone additional parameter.

26. The workout machine system of any one of examples 1-25 wherein theprocessing component is further configured to:

-   -   receive further measurements indicative of power output as the        adaptive training program is performed;    -   identify further work values based on the further measurements        indicative of power output;    -   update the ability function to further fit the further work        values;    -   update one or more of the given parameters or one or more of the        at least one additional parameter based on the updated ability        function;    -   generate an updated adaptive training program based on the        updated parameters; and    -   update the output or settings of the workout apparatus based on        the updated adaptive training program.

27. A method for providing an adaptive training program, the methodcomprising:

-   -   obtaining measurements indicative of power output;    -   identifying a work value for each particular window size, of        multiple window size durations of the measurements indicative of        power output, by:        -   identifying a largest integration, of a function specified            by the measurements indicative of power output, for an            interval matching the particular window size;    -   fitting, as an ability function, a function to the work values        identified for the multiple window size durations;    -   computing a value for at least one previously unspecified        parameter for a training session, such that values of a workout        function, that uses the previously unspecified parameter, do not        exceed corresponding values of the ability function for any time        during a duration for the training session;    -   using the at least one previously unspecified parameter to        generate the adaptive training program; and    -   providing output or automatic workout settings based on the        adaptive training program.

28. The method of example 27, wherein the measurements indicative ofpower output are obtained through manual entry by a user.

29. The method of example 27, wherein the measurements indicative ofpower output are obtained through recorded workout statistics taken by awearable fitness tracker.

30. The method of example 27, wherein the measurements indicative ofpower output are obtained through an instrument system integrated into aworkout apparatus.

31. The method of any one of examples 27-30,

-   -   wherein the method is implemented in relation to multiple        workout machines connected via a network;    -   wherein the measurements indicative of power output are taken        from a first of the networked workout machines;    -   wherein a second set of measurements indicative of power output        are taken, from a second of the networked workout machines,        which are transformed into additional work values for        corresponding window sizes and are weighted based on an age of        the second set of measurements; and    -   wherein the fitting the function to the work values identified        for the multiple window size durations further also comprises        fitting the function to the additional work values.

32. The method of example 31, wherein the fitting the function to thework values and to the additional work values comprises:

-   -   selecting, for each particular window size, the largest value,        of the work values and to the additional work values,        corresponding to that particular window size; and    -   fitting the function to the selected largest values for the        window sizes.

33. The method of any one of examples 27-32, wherein the method isperformed by a server system connected to multiple workout machines.

34. The method of any one of examples 27-33, wherein generating theadaptive training program is in response to an identificationcomprising:

-   -   a code indicative of a user or of a workout apparatus, supplied        by the user, by a mobile device associated with the user, or by        the workout apparatus.

35. The method of example 34,

-   -   wherein the generation of the adaptive training program is in        response to the identification comprising the code indicative of        the workout apparatus; and    -   wherein the code indicative of the workout apparatus is supplied        to the mobile device through an image capture system on the        mobile device capturing an alphanumeric code, bar code, or QR        code displayed in conjunction with the workout apparatus.

36. The method of example 34,

-   -   wherein the generation of the adaptive training program is in        response to the identification comprising the code indicative of        the user; and    -   wherein the code indicative of the user is supplied by the        mobile device.

37. The method of any one of examples 27-30 or 36, wherein the method isperformed by a workout machine.

38. The method of any one of examples 27-30 or 36, wherein the method isperformed by a mobile device associated with a user of a workoutapparatus.

39. The method of any one of examples 27-38, wherein providing theoutput or automatic workout settings comprises providing the output to aserver system, the output comprising at least an indication of the atleast one additional parameter.

40. The method of any one of examples 27-39, wherein providing theoutput or automatic workout settings comprises providing output to amobile device, the output comprising at least an indication of the atleast one previously unspecified parameter.

41. The method of any one of examples 27-40, wherein providing theoutput or automatic workout settings comprises providing the output to amobile device, the output comprising data to be operated on by executionof instructions on the mobile device, configured to provide a displayfor a user to implement the adaptive training program.

42. The method of any one of examples 27-41, wherein providing theoutput or automatic workout settings comprises providing the output to aworkout apparatus, the output configured to cause a display of theworkout apparatus to provide instructions for the adaptive trainingprogram.

43. The method of any one of examples 27-42, wherein providing theoutput or automatic workout settings comprises providing the automaticworkout settings to a workout apparatus, wherein the workout apparatusautomatically implements the adaptive training program based on theautomatic workout settings such that the workout apparatus implementsthe given parameters and the at least one previously unspecifiedparameter.

44. The method of any one of examples 27-43, wherein providing theoutput or automatic workout settings comprises providing the output to aserver system or a mobile device, wherein the output is stored inassociation with a user profile comprising one or more of:

-   -   at least some of the identified work values;    -   the ability function;    -   statistics of the adaptive training program;    -   measures of actual performance during the training session;    -   biographic specifics; or    -   any combination thereof.

45. The method of any one of examples 27-44, further comprisingproviding an internet-based interface, accessible through a web browseror mobile device application, wherein the internet-based interfaceprovides access to a user profile associated with multiple workoutsperformed by a user.

46. The method of any of any one of examples 27-45, wherein themeasurements indicative of power output comprise one or more of:

-   -   speed;    -   revolutions-per-minute;    -   resistance;    -   incline;    -   distance,    -   duration;    -   weight,    -   repetitions; or    -   any combination thereof.

47. The method of any one of examples 27-46 further comprising:

-   -   based on the ability function, determining a critical power and        finite work capacity for a user associated with the        measurements; and    -   modifying the ability function into a combined linear and        logarithmic function that uses the critical power and finite        work capacity.

48. The method of any one of examples 31 or 32,

-   -   wherein the method is performed by a server system connected to        the network; and    -   wherein the server system obtains the additional work values        stored in association with a user profile for the user and        generates the adaptive training program, both in response to an        authentication of the user.

49. The method of any one of examples 27-48, wherein the workoutfunction is defined for an interval type workout.

50. The method of example 49, wherein the given parameters for theinterval type workout function includes at least three of: number ofintervals, interval duration, power output during intervals, restduration, power output during rests, or any combination thereof.

51. The method of any one of examples 27-48, wherein the workoutfunction is defined for a skip interval type workout.

52. The method of example 51, wherein the given parameters for the skipinterval type workout function includes at least three of: number ofintervals, duration of peak work during interval, duration of short resttime within an interval, power output during interval peak, restduration between intervals, power output during rests, or anycombination thereof.

53. The method of any one of examples 27-52,

-   -   wherein computing the value for the at least one previously        unspecified parameter comprises:        -   obtaining the workout function, from a set of pre-defined            functions, based on an indicated workout type for the            adaptive training program;        -   filling in one or more given parameters in the pre-defined            workout function; and        -   solving for the at least one previously unspecified            parameter, wherein the at least one previously unspecified            parameter includes exactly one parameter; and    -   wherein using the at least one previously unspecified parameter        to generate the adaptive training program comprises:        -   selecting data to display instructing a user to perform a            workout based on at least the at least one previously            unspecified parameter; or        -   specifying the output or automatic workout settings based on            the at least one previously unspecified parameter.

54. The method of example 53, wherein filling in the one or more givenparameters in the pre-defined function comprises using one or moredefault values for one or more of the workout function parameters thatare not included in the given parameters and are not included in the atleast one previously unspecified parameter.

55. The method of any one of examples 27-54, further comprising:

-   -   receiving further measurements indicative of power output as the        adaptive training program is performed;    -   identifying further work values based on the further        measurements indicative of power output;    -   updating the ability function to further fit the further work        values;    -   updating one or more parameters of the workout function based on        a comparison of the workout function to the updated ability        function;    -   generating an updated adaptive training program based on the        updated parameters; and    -   updating the output or automatic workout settings based on the        updated adaptive training program.

56. A computer-readable storage medium storing instructions that, whenexecuted by a computing system, cause the computing system to performoperations for automatically providing an adaptive training program, theoperations comprising:

-   -   obtaining given parameters for a training session;    -   obtaining measurements indicative of power output;    -   generating the adaptive training program by:        -   determining, based on the measurements indicative of power            output, an ability function or critical power assessment;            and        -   using (A) the ability function or critical power assessment            and (B) the given parameters, generating the training            program including a value for at least one previously            unspecified parameter; and    -   providing the adaptive training program.

57. The computer-readable storage medium of example 56, wherein theoperations further comprise identifying a work value for each particularwindow size, of multiple window size durations of the measurementsindicative of power output, by identifying a largest integration, of afunction specified by the measurements indicative of power output, foran interval matching the particular window size.

58. The computer-readable storage medium of example 57, whereindetermining the ability function or critical power assessment comprisesfitting a function to the work values identified for the multiple windowsize durations.

59. The computer-readable storage medium of example 57, whereingenerating the training program is performed by computing values foreach of the at least one previously unspecified parameter, such thatvalues of a workout function that uses the at least one previouslyunspecified parameter do not exceed corresponding values of the abilityfunction for any time during a duration for the training session.

60. The computer-readable storage medium of any one of examples 56-59,wherein the measurements indicative of power output are obtained throughmanual entry by a user.

61. The computer-readable storage medium of any one of examples 56-59,wherein the measurements indicative of power output are obtained throughrecorded workout statistics taken by a wearable fitness tracker.

62. The computer-readable storage medium of any one of examples 56-59,wherein the measurements indicative of power output are obtained throughan instrument system integrated into a workout apparatus.

63. The computer-readable storage medium of example 58,

-   -   wherein the computing system is coupled to a network connecting        multiple workout machines;    -   wherein the measurements indicative of power output are taken by        a first of the networked workout machines;    -   wherein a second set of measurements indicative of power output        are taken, by a second of the networked workout machines, which        are transformed into additional work values for corresponding        window sizes; and    -   wherein the fitting the function to the work values identified        for the multiple window size durations further comprises also        fitting the function to the additional work values.

64. The computer-readable storage medium of example 58,

-   -   wherein the computing system is coupled to a network connecting        multiple workout machines;    -   wherein the measurements indicative of power output are taken by        a first of the networked workout machines;    -   wherein a second set of measurements indicative of power output        are taken, by a second of the networked workout machines, which        are transformed into additional work values for corresponding        window sizes; and    -   wherein the fitting the function to the work values identified        for the multiple window size durations further comprises fitting        the function also to the additional work values; and    -   wherein the fitting the function to the work values and to the        additional work values comprises:        -   selecting, for each particular window size, the largest            value, of the work values and to the additional work values,            corresponding to that particular window size; and        -   fitting the function to the selected largest values for the            window sizes.

65. The computer-readable storage medium of any one of examples 56-64,wherein the operations are performed by a server system connected tomultiple workout machines.

66. The computer-readable storage medium of any one claims 56-65,wherein generating the adaptive training program is in response to anidentification comprising:

-   -   a code indicative of a user or of a workout apparatus, supplied        by the user, by a mobile device associated with the user, or by        the workout apparatus.

67. The computer-readable storage medium of example 66,

-   -   wherein the generation of the adaptive training program is in        response to the identification comprising the code indicative of        the workout apparatus; and    -   wherein the code indicative of the workout apparatus is supplied        to the mobile device through an image capture system on the        mobile device capturing an alphanumeric code, bar code, or QR        code displayed in conjunction with the workout apparatus.

68. The computer-readable storage medium of example 66,

-   -   wherein the generation of the adaptive training program is in        response to the identification comprising the code indicative of        the user; and    -   wherein the code indicative of the user is supplied by        communication of the code, to the workout apparatus, by the        mobile device.

69. The computer-readable storage medium of any one of examples 56-64,wherein the operations are performed by a workout machine.

70. The computer-readable storage medium of any one of examples 56-64,wherein the operations are performed by a mobile device associated witha user of a workout apparatus.

71. The computer-readable storage medium of any one of examples 56-70,wherein providing the adaptive training program comprises providingoutput to a server system, the output comprising at least an indicationof the at least one previously unspecified parameter.

72. The computer-readable storage medium of any one of examples 56-71,wherein providing the adaptive training program comprises providingoutput to a mobile device, the output comprising at least an indicationof the at least one previously unspecified parameter.

73. The computer-readable storage medium of any one of examples 56-72,wherein providing the adaptive training program comprises providingoutput to a mobile device, the output comprising data to be operated onby execution of instructions on the mobile device, configured to providea display for a user to implement the adaptive training program.

74. The computer-readable storage medium of any one of examples 56-73,wherein providing the adaptive training program comprises providingoutput to a workout apparatus, the output configured to cause a displayof the workout apparatus to provide instructions for the adaptivetraining program.

75. The computer-readable storage medium of any one of examples 56-74,wherein providing the adaptive training program comprises providingautomatic workout settings to a workout apparatus, wherein the workoutapparatus automatically implements the adaptive training program basedon the automatic workout settings.

76. The computer-readable storage medium of any one of examples 56-75,wherein the operations further comprise providing output to a serversystem or a mobile device, the output being stored in association with auser profile comprising one or more of:

-   -   at least some of the identified work values;    -   the ability function;    -   statistics of the adaptive training program;    -   measures of actual performance during the training session;    -   biographic specifics; or    -   any combination thereof.

77. The computer-readable storage medium of any one of examples 56-76,wherein the operations further comprise providing an internet-basedinterface, accessible through a web browser or mobile deviceapplication, wherein the internet-based interface provides access to auser profile associated with multiple workouts performed by a user.

78. The computer-readable storage medium of any of any one of examples56-77, wherein the measurements indicative of power output comprise oneor more of:

-   -   speed;    -   revolutions-per-minute;    -   resistance;    -   incline;    -   distance and duration;    -   weight and repetitions; or    -   any combination thereof.

79. The computer-readable storage medium of any one of examples 56-78wherein the ability function is a linear function.

80. The computer-readable storage medium of any one of examples 56-79,

-   -   wherein the operations further comprise computing the value for        the at least one previously unspecified parameter by:        -   obtaining a workout function, from a set of pre-defined            functions, based on an indicated workout type for the            adaptive training program;        -   filling the given parameters into the workout function; and        -   solving for the at least one previously unspecified            parameter, wherein the at least one previously unspecified            parameter includes exactly one parameter; and    -   wherein generating the adaptive training program comprises:        -   selecting data to display instructing a user to perform a            workout based on at least the at least one previously            unspecified parameter; or        -   specifying automatic workout settings based on the at least            one previously unspecified parameter.

81. The computer-readable storage medium of example 53, wherein fillingin the one or more given parameters in the pre-defined functioncomprises using one or more default values for one or more of theworkout function parameters that are not included in the givenparameters and are not included in the at least one previouslyunspecified parameter.

82. The computer-readable storage medium of any one of examples 56-81,wherein the operations further comprise:

-   -   receiving further measurements indicative of power output as the        adaptive training program is performed;    -   identifying further work values based on the further        measurements indicative of power output;    -   updating the ability function to further fit the further work        values; and    -   generating an updated adaptive training program based on the        ability function.

83. A method performed by a mobile device for providing an adaptivetraining program, the method comprising:

-   -   obtaining measurements indicative of power output;    -   identifying a work value for each particular window size, of        multiple window size durations of the measurements indicative of        power output, by:        -   identifying a largest integration, of a function specified            by the measurements indicative of power output, for an            interval matching the particular window size;    -   fitting, as an ability function, a function to the work values        identified for the multiple window size durations;    -   computing a value for at least one previously unspecified        parameter for a training session, such that values of a workout        function, that uses the previously unspecified parameter, do not        exceed corresponding values of the ability function for any time        during a duration for the training session;    -   using the at least one previously unspecified parameter to        generate the adaptive training program; and    -   transmitting automatic workout settings based on the adaptive        training program to a workout apparatus, wherein the workout        apparatus automatically implements the adaptive training program        based on the automatic workout settings.

Several implementations of the disclosed technology are described abovein reference to the figures. The computing devices on which thedescribed technology may be implemented can include one or more centralprocessing units, memory, input devices (e.g., keyboard and pointingdevices), output devices (e.g., display devices), storage devices (e.g.,disk drives), and network devices (e.g., network interfaces). The memoryand storage devices are computer-readable storage media that can storeinstructions that implement at least portions of the describedtechnology. In addition, the data structures and message structures canbe stored or transmitted via a data transmission medium, such as asignal on a communications link. Various communications links can beused, such as the Internet, a local area network, a wide area network,or a point-to-point dial-up connection. Thus, computer-readable mediacan comprise computer-readable storage media (e.g., “non-transitory”media) and computer-readable transmission media.

Reference in this specification to “implementations” (e.g. “someimplementations,” “various implementations,” “one implementation,” “animplementation,” etc.) means that a particular feature, structure, orcharacteristic described in connection with the implementation isincluded in at least one implementation of the disclosure. Theappearances of these phrases in various places in the specification arenot necessarily all referring to the same implementation, nor areseparate or alternative implementations mutually exclusive of otherimplementations. Moreover, various features are described which may beexhibited by some implementations and not by others. Similarly, variousrequirements are described which may be requirements for someimplementations but not for other implementations.

As used herein, being above a threshold means that a value for an itemunder comparison is above a specified other value, that an item undercomparison is among a certain specified number of items with the largestvalue, or that an item under comparison has a value within a specifiedtop percentage value. As used herein, being below a threshold means thata value for an item under comparison is below a specified other value,that an item under comparison is among a certain specified number ofitems with the smallest value, or that an item under comparison has avalue within a specified bottom percentage value. As used herein, beingwithin a threshold means that a value for an item under comparison isbetween two specified other values, that an item under comparison isamong a middle specified number of items, or that an item undercomparison has a value within a middle specified percentage range.Relative terms, such as high or unimportant, when not otherwise defined,can be understood as assigning a value and determining how that valuecompares to an established threshold. For example, the phrase “selectinga fast connection” can be understood to mean selecting a connection thathas a value assigned corresponding to its connection speed that is abovea threshold.

As used herein, the word “or” refers to any possible permutation of aset of items. For example, the phrase “A, B, or C” refers to at leastone of A, B, C, or any combination thereof, such as any of: A; B; C; Aand B; A and C; B and C; A, B, and C; or multiple of any item such as Aand A; B, B, and C; A, A, B, C, and C; etc.

Although the subject matter has been described in language specific tostructural features or methodological acts, it is to be understood thatthe subject matter defined in the appended claims is not necessarilylimited to the specific features or acts described above. Specificembodiments and implementations have been described herein for purposesof illustration, but various modifications can be made without deviatingfrom the scope of the embodiments and implementations. The specificfeatures and acts described above are disclosed as example forms ofimplementing the claims that follow. Accordingly, the embodiments andimplementations are not limited except as by the appended claims.

Any patents, patent applications, and other references noted above areincorporated herein by reference. Aspects can be modified, if necessary,to employ the systems, functions, and concepts of the various referencesdescribed above to provide yet further implementations. If statements orsubject matter in a document incorporated by reference conflicts withstatements or subject matter of this application, then this applicationshall control.

1. A workout machine system for automatically providing an adaptive training program, the system comprising: a workout apparatus that implements a training session of a particular type, with given parameters; an instrument system, integrated with the workout apparatus, that obtains measurements indicative of power output; and a processing component configured to generate the adaptive training program by: identifying a work value for each particular window size, of multiple window size durations, within the measurements indicative of power output, by: identifying a largest integration, of a function specified by the measurements indicative of power output, for an interval matching the particular window size; fitting, as an ability function, a function to the identified work values; applying, to a workout function defined for the particular type of the training session, the given parameters and at least one additional parameter, such that the value of the workout function does not exceed the value of the ability function for any time during a duration for the training session; and using the given parameters and at least one additional parameter to generate the adaptive training program; wherein output or settings of the workout apparatus are automatically provided based on the adaptive training program.
 2. The workout machine system of claim 1, wherein the workout machine system further comprises multiple workout machines connected via a network; and wherein the fitting the function to the work values identified for the multiple window size durations further comprises also fitting the function to additional work values identified for the multiple window size durations based on measurements indicative of power output taken on one of the networked workout machines other than the workout apparatus.
 3. The workout machine system of claim 2, wherein the fitting the function to the work values and to the additional work values comprises: selecting, for each particular window size, the largest value, of the work values and to the additional work values, corresponding to that particular window size; and fitting the function to the selected largest values for the window sizes.
 4. The workout machine system of claim 1, wherein the processing component is a server system connected to the workout apparatus via a network.
 5. The workout machine system of claim 1, wherein the processing component generates the adaptive training program in response to an identification comprising a code indicative of the workout apparatus; and wherein the code indicative of the workout apparatus is supplied to a mobile device associated with the user through an image capture system, on the mobile device, capturing an alphanumeric code, a bar code, or a QR code displayed in conjunction with the workout apparatus.
 6. The workout machine system of claim 1, wherein the automatically providing the output or settings of the workout apparatus comprises providing the output to a server system, the output comprising at least an indication of the at least one additional parameter.
 7. The workout machine system of claim 1, wherein the automatically providing the output or settings of the workout apparatus comprises providing the output to the workout apparatus, the output configured to cause a display of the workout apparatus to provide instructions for the adaptive training program.
 8. The workout machine system of claim 1, wherein the automatically providing the output or settings of the workout apparatus comprises implementing, on the workout apparatus, the adaptive training program based on the settings such that the workout apparatus implements the given parameters and the at least one additional parameter.
 9. The workout machine system of claim 1, wherein the particular type is an interval type workout and wherein the given parameters for the interval type workout includes one or more of: number of intervals, interval duration, power output during intervals, rest duration, power output during rests, or any combination thereof.
 10. The workout machine system of claim 1, wherein the particular type is a skip interval type workout and wherein the given parameters for the skip interval type workout include one or more of: number of intervals, duration of peak work during interval, duration of short rest time within an interval, power output during interval peak, rest duration between intervals, power output during rests, or any combination thereof.
 11. The workout machine system of claim 1, wherein applying, to the workout function defined for the particular type of the training session, the given parameters and at least one additional parameter comprises: obtaining a pre-defined function for the particular type of the training session; filling in the given parameters in the pre-defined function; and solving for the at least one additional parameter.
 12. The workout machine system of claim 11, wherein filling in the given parameters in the pre-defined function comprises using one or more default values for one or more of the function parameters that are not included in the given parameters and are not included in the at least one additional parameter.
 13. The workout machine system of claim 1 wherein the processing component is further configured to: receive further measurements indicative of power output as the adaptive training program is performed; identify further work values based on the further measurements indicative of power output; update the ability function to further fit the further work values; update one or more of the given parameters or one or more of the at least one additional parameter based on the updated ability function; generate an updated adaptive training program based on the updated parameters; and update the output or settings of the workout apparatus based on the updated adaptive training program.
 14. A method for providing an adaptive training program, the method comprising: obtaining measurements indicative of power output; identifying a work value for each particular window size, of multiple window size durations, within the measurements indicative of power output by identifying a largest integration, of a function specified by the measurements indicative of power output, for an interval matching the particular window size; fitting, as an ability function, a function to the work values identified for the multiple window size durations; computing a value for at least one previously unspecified parameter for a training session, such that values of a workout function, that uses the previously unspecified parameter, do not exceed corresponding values of the ability function for any time during a duration for the training session; using the at least one previously unspecified parameter to generate the adaptive training program; and providing output or automatic workout settings based on the adaptive training program.
 15. The method of claim 14, wherein the measurements indicative of power output are obtained through one or more of: manual entry by a user; workout statistics taken by a wearable fitness tracker; an instrument system integrated into a workout apparatus; or any combination thereof.
 16. The method of claim 14, wherein the method is implemented in relation to multiple workout machines connected via a network; wherein the measurements indicative of power output are taken from a first of the networked workout machines; wherein a second set of measurements indicative of power output are taken, from a second of the networked workout machines, which are transformed into additional work values for corresponding window sizes and are weighted based on an age of the second set of measurements; and wherein the fitting the function to the work values identified for the multiple window size durations further also comprises fitting the function to the weighted additional work values.
 17. The method of claim 16, wherein the fitting the function to the work values and to the additional work values comprises: selecting, for each particular window size, the largest value of either the work values or the additional work values, corresponding to that particular window size; and fitting the function to the selected largest values for the window sizes.
 18. The method of claim 14, wherein the generation of the adaptive training program is in response to an identification comprising a code indicative of the user; and wherein the code indicative of the user is supplied by a mobile device associated with a user of a workout apparatus.
 19. The method of claim 14, wherein the method is performed by a mobile device associated with a user of a workout apparatus.
 20. The method of claim 14, wherein providing the output or automatic workout settings comprises causing output to be provided on a mobile device, the output based on the at least one previously unspecified parameter.
 21. The method of claim 14, wherein providing the output or automatic workout settings comprises providing the output to a server system or a mobile device, wherein the output is stored in association with a user profile comprising one or more of: at least some of the identified work values; the ability function; statistics of the adaptive training program; measures of actual performance during the training session; biographic specifics; or any combination thereof.
 22. The method of claim 14 further comprising: based on the ability function, determining a critical power and finite work capacity for a user associated with the measurements; and modifying the ability function to be a combined linear and logarithmic function that uses the critical power and finite work capacity.
 23. A computer-readable storage medium storing instructions that, when executed by a computing system, cause the computing system to perform operations for automatically providing an adaptive training program, the operations comprising: obtaining given parameters for a training session; obtaining measurements indicative of power output; generating the adaptive training program by: determining, based on the measurements indicative of power output, an ability function or critical power assessment; and generating, using (A) the ability function or critical power assessment and (B) the given parameters, the training program including a value for at least one previously unspecified parameter; and providing the adaptive training program.
 24. The computer-readable storage medium of claim 23, wherein the operations further comprise identifying a work value for each particular window size, of multiple window size durations, within the measurements indicative of power output, wherein identifying each particular work value for the particular window size comprises identifying a largest integration, of a function specified by the measurements indicative of power output, for an interval matching the particular window size.
 25. The computer-readable storage medium of claim 23, wherein generating the adaptive training program is performed by computing values for each of the at least one previously unspecified parameter, such that values of a workout function that uses the at least one previously unspecified parameter do not exceed corresponding values of the ability function for any time during a duration for the training session.
 26. The computer-readable storage medium of claim 23, wherein the operations further comprise computing the value for the at least one previously unspecified parameter by: obtaining a pre-defined workout function; filling the given parameters into the pre-defined workout function; and solving for the at least one previously unspecified parameter. 